Cascading valorization of defatted rice bran for lactic acid fermentation and biogas production
Bibliographic record
Abstract
This study investigated the integrated valorization of defatted rice bran (DRB) by converting it into lactic acid (LA) and subsequently utilizing the residues from LA production for biomethane generation through anaerobic digestion (AD). Processing 480 kg of DRB resulted in the production of 70 L of pure LA and generated significant waste streams, primarily consisting of 572 kg of decanted hydrolysate pellet (Pellet DEC) and 220 kg of microfiltration retentate (Retentate MF). Exceptionally high methane yields of 374‒434 LN kgVS-1 were observed for residues from LA fermentation in biochemical methane potential tests, indicating their high potential for biogas production. During long-term semi-continuous AD, varying organic loading rates (OLRs) from 0.5‒2.5 kgVS m-3 d⁻¹ demonstrated feedstock- and OLR-dependent methane production. Reactor failure at higher OLRs was attributed to the accumulation of total ammoniacal nitrogen (TAN). The co-digestion of Pellet DEC and Retentate MF proved to be more resilient, with OLRs up to 2 kgVS m-3 d-1, mitigating TAN inhibition. Methane yields, ranging from 265‒334 LN kgVS-1 before reaching inhibitory OLR levels, were higher than those found in the literature. Process integration has emerged as a promising approach because the biogas generated from residues could effectively offset the energy demands of LA production. Supported by life cycle assessment, the integrated processes showed a 67% lower environmental impact at the midpoint and a 71% lower environmental impact at the endpoint, along with an 80% reduction in energy costs compared to the standalone LA production. Results proved a significant enhancement of the sustainability and economic viability of this integrated biorefinery approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".